This thesis presents a novel approach to how a high dimensional humanoid robot of 18 dimensions can learn
within a few hours to control its body so that it is able to perform simple tasks such as rolling around or to sit
up. The method is robust and works equally well when an arm is removed, and in a case where the robot was
trained to use two arms and one was removed it quickly adapted to its new body. The robot is equipped with
an accelerometer that measures the tilt of the torso in 2 dimensions. This "tilt"-space is divided into a discrete
set of states, and the way in which the dimensionality of the servo-space is made irrelevant is to only allow one
servo-conguration per state. These congurations are evolved using a Self-Organizing Map, while an Articial
Curiosity-driven Reinforcement Learner chooses what state to state transitions to attempt. An additional
parameter is added in a nal experiment, to see if the agent can even learn to stand. This experiment was
however unsuccessful.

BibTeX @mastersthesis{Loviken2015,author={Loviken, Pontus},title={Curiosity based Self-Organization of Humanoid Robot},abstract={This thesis presents a novel approach to how a high dimensional humanoid robot of 18 dimensions can learn
within a few hours to control its body so that it is able to perform simple tasks such as rolling around or to sit
up. The method is robust and works equally well when an arm is removed, and in a case where the robot was
trained to use two arms and one was removed it quickly adapted to its new body. The robot is equipped with
an accelerometer that measures the tilt of the torso in 2 dimensions. This "tilt"-space is divided into a discrete
set of states, and the way in which the dimensionality of the servo-space is made irrelevant is to only allow one
servo-conguration per state. These congurations are evolved using a Self-Organizing Map, while an Articial
Curiosity-driven Reinforcement Learner chooses what state to state transitions to attempt. An additional
parameter is added in a nal experiment, to see if the agent can even learn to stand. This experiment was
however unsuccessful.},publisher={Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola},place={Göteborg},year={2015},series={Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2015:63},keywords={Self-organized robotics, Reinforcement Learning, High DoF, Physical Environment, Humanoid Robot, Bioloid, Embodiment, Articial Curiosity, Kulback-Liebler Divergence, Self-organizing map, HyperSOM.},}

RefWorks RT GenericSR ElectronicID 219219A1 Loviken, PontusT1 Curiosity based Self-Organization of Humanoid RobotYR 2015AB This thesis presents a novel approach to how a high dimensional humanoid robot of 18 dimensions can learn
within a few hours to control its body so that it is able to perform simple tasks such as rolling around or to sit
up. The method is robust and works equally well when an arm is removed, and in a case where the robot was
trained to use two arms and one was removed it quickly adapted to its new body. The robot is equipped with
an accelerometer that measures the tilt of the torso in 2 dimensions. This "tilt"-space is divided into a discrete
set of states, and the way in which the dimensionality of the servo-space is made irrelevant is to only allow one
servo-conguration per state. These congurations are evolved using a Self-Organizing Map, while an Articial
Curiosity-driven Reinforcement Learner chooses what state to state transitions to attempt. An additional
parameter is added in a nal experiment, to see if the agent can even learn to stand. This experiment was
however unsuccessful.PB Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola,T3 Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2015:63LA engLK http://publications.lib.chalmers.se/records/fulltext/219219/219219.pdfOL 30